Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths are found and used for adversarial training. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions and allows input of a shared vehicle feature, showing that inclusion of this module leads to improved trajectory prediction both with and without the presence of a vehicle. Through evaluation on various datasets, we demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.

中文翻译：

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概率人群 GAN：使用图车辆-行人注意网络进行多模式行人轨迹预测

理解和预测行人的意图对于使自动驾驶汽车和移动机器人能够在人群中导航至关重要。当我们考虑行人运动的不确定性和多模态，以及人群成员之间的隐性交互（包括对车辆的任何反应）时，这个问题变得越来越复杂。我们的方法 Probabilistic Crowd GAN 扩展了最近在轨迹预测方面的工作，将循环神经网络 (RNN) 与混合密度网络 (MDN) 相结合以输出概率多模态预测，从中找到可能的模态路径并用于对抗性训练。我们还建议使用图形车辆-行人注意网络 (GVAT)，它可以模拟社交互动并允许输入共享车辆特征，表明包含该模块可以在有和没有车辆存在的情况下改进轨迹预测。通过对各种数据集的评估，我们展示了对现有最先进轨迹预测方法的改进，并说明了如何直接对人群交互的真正多模态和不确定性进行建模。